Critical applications, such as in the medical field, require the rapid
provision of additional information to interpret decisions made by deep
learning methods. In this work, we propose a fast and accurate method to
visualize activations of classification and semantic segmentation networks by
stitching them with a GAN generator utilizing convolutions. We test our
approach on images of animals from the AFHQ wild dataset and real-world digital
pathology scans of stained tissue samples. Our method provides comparable
results to established gradient descent methods on these datasets while running
about two orders of magnitude faster.
( 2
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We study online Reinforcement Learning (RL) in non-stationary input-driven
environments, where a time-varying exogenous input process affects the
environment dynamics. Online RL is challenging in such environments due to
catastrophic forgetting (CF). The agent tends to forget prior knowledge as it
trains on new experiences. Prior approaches to mitigate this issue assume task
labels (which are often not available in practice) or use off-policy methods
that can suffer from instability and poor performance.
We present Locally Constrained Policy Optimization (LCPO), an on-policy RL
approach that combats CF by anchoring policy outputs on old experiences while
optimizing the return on current experiences. To perform this anchoring, LCPO
locally constrains policy optimization using samples from experiences that lie
outside of the current input distribution. We evaluate LCPO in two gym and
computer systems environments with a variety of synthetic and real input
traces, and find that it outperforms state-of-the-art on-policy and off-policy
RL methods in the online setting, while achieving results on-par with an
offline agent pre-trained on the whole input trace.
( 2
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Bilevel optimization has been developed for many machine learning tasks with
large-scale and high-dimensional data. This paper considers a constrained
bilevel optimization problem, where the lower-level optimization problem is
convex with equality and inequality constraints and the upper-level
optimization problem is non-convex. The overall objective function is
non-convex and non-differentiable. To solve the problem, we develop a
gradient-based approach, called gradient approximation method, which determines
the descent direction by computing several representative gradients of the
objective function inside a neighborhood of the current estimate. We show that
the algorithm asymptotically converges to the set of Clarke stationary points,
and demonstrate the efficacy of the algorithm by the experiments on
hyperparameter optimization and meta-learning.
( 2
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Contrary to its original interpretation as a facilitator of knowledge
transfer from one model to another, some recent studies have suggested that
knowledge distillation (KD) is instead a form of regularization. Perhaps the
strongest support of all for this claim is found in its apparent similarities
with label smoothing (LS). This paper investigates the stated equivalence of
these two methods by examining the predictive uncertainties of the models they
train. Experiments on four text classification tasks involving teachers and
students of different capacities show that: (a) In most settings, KD and LS
drive model uncertainty (entropy) in completely opposite directions, and (b) In
KD, the student's predictive uncertainty is a direct function of that of its
teacher, reinforcing the knowledge transfer view.
( 2
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This work investigates the intersection of cross modal learning and semi
supervised learning, where we aim to improve the supervised learning
performance of the primary modality by borrowing missing information from an
unlabeled modality. We investigate this problem from a Nadaraya Watson (NW)
kernel regression perspective and show that this formulation implicitly leads
to a kernelized cross attention module. To this end, we propose The Attention
Patch (TAP), a simple neural network plugin that allows data level knowledge
transfer from the unlabeled modality. We provide numerical simulations on three
real world datasets to examine each aspect of TAP and show that a TAP
integration in a neural network can improve generalization performance using
the unlabeled modality.
( 2
min )
There has been much recent progress in forecasting the next observation of a
linear dynamical system (LDS), which is known as the improper learning, as well
as in the estimation of its system matrices, which is known as the proper
learning of LDS. We present an approach to proper learning of LDS, which in
spite of the non-convexity of the problem, guarantees global convergence of
numerical solutions to a least-squares estimator. We present promising
computational results.
( 2
min )
This work investigates the intersection of cross modal learning and semi
supervised learning, where we aim to improve the supervised learning
performance of the primary modality by borrowing missing information from an
unlabeled modality. We investigate this problem from a Nadaraya Watson (NW)
kernel regression perspective and show that this formulation implicitly leads
to a kernelized cross attention module. To this end, we propose The Attention
Patch (TAP), a simple neural network plugin that allows data level knowledge
transfer from the unlabeled modality. We provide numerical simulations on three
real world datasets to examine each aspect of TAP and show that a TAP
integration in a neural network can improve generalization performance using
the unlabeled modality.
( 2
min )
We present a variety of novel information-theoretic generalization bounds for
learning algorithms, from the supersample setting of Steinke & Zakynthinou
(2020)-the setting of the "conditional mutual information" framework. Our
development exploits projecting the loss pair (obtained from a training
instance and a testing instance) down to a single number and correlating loss
values with a Rademacher sequence (and its shifted variants). The presented
bounds include square-root bounds, fast-rate bounds, including those based on
variance and sharpness, and bounds for interpolating algorithms etc. We show
theoretically or empirically that these bounds are tighter than all
information-theoretic bounds known to date on the same supersample setting.
( 2
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Message Passing Neural Networks (MPNNs) are instances of Graph Neural
Networks that leverage the graph to send messages over the edges. This
inductive bias leads to a phenomenon known as over-squashing, where a node
feature is insensitive to information contained at distant nodes. Despite
recent methods introduced to mitigate this issue, an understanding of the
causes for over-squashing and of possible solutions are lacking. In this
theoretical work, we prove that: (i) Neural network width can mitigate
over-squashing, but at the cost of making the whole network more sensitive;
(ii) Conversely, depth cannot help mitigate over-squashing: increasing the
number of layers leads to over-squashing being dominated by vanishing
gradients; (iii) The graph topology plays the greatest role, since
over-squashing occurs between nodes at high commute (access) time. Our analysis
provides a unified framework to study different recent methods introduced to
cope with over-squashing and serves as a justification for a class of methods
that fall under `graph rewiring'.
( 2
min )
This work studies the pure-exploration setting for the convex hull
feasibility (CHF) problem where one aims to efficiently and accurately
determine if a given point lies in the convex hull of means of a finite set of
distributions. We give a complete characterization of the sample complexity of
the CHF problem in the one-dimensional setting. We present the first
asymptotically optimal algorithm called Thompson-CHF, whose modular design
consists of a stopping rule and a sampling rule. In addition, we provide an
extension of the algorithm that generalizes several important problems in the
multi-armed bandit literature. Finally, we further investigate the Gaussian
bandit case with unknown variances and address how the Thompson-CHF algorithm
can be adjusted to be asymptotically optimal in this setting.
( 2
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The recipe behind the success of deep learning has been the combination of
neural networks and gradient-based optimization. Understanding the behavior of
gradient descent however, and particularly its instability, has lagged behind
its empirical success. To add to the theoretical tools available to study
gradient descent we propose the principal flow (PF), a continuous time flow
that approximates gradient descent dynamics. To our knowledge, the PF is the
only continuous flow that captures the divergent and oscillatory behaviors of
gradient descent, including escaping local minima and saddle points. Through
its dependence on the eigendecomposition of the Hessian the PF sheds light on
the recently observed edge of stability phenomena in deep learning. Using our
new understanding of instability we propose a learning rate adaptation method
which enables us to control the trade-off between training stability and test
set evaluation performance.
( 2
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https://www.theverge.com/2023/2/7/23587454/microsoft-bing-edge-chatgpt-ai
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From Article:
Getty Images new lawsuit claims that Stability AI, the company behind Stable Diffusion's AI image generator, stole 12 million Getty images with their captions, metadata, and copyrights "without permission" to "train its Stable Diffusion algorithm."
The company has asked the court to order Stability AI to remove violating images from its website and pay $150,000 for each.
However, it would be difficult to prove all the violations. Getty submitted over 7,000 images, metadata, and copyright registration, used by Stable Diffusion.
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📢 News 📢
Pythae 0.1.0 is now out and supports distributed training using PyTorch DDP !
Train your favorite Variational Autoencoders (VAEs) faster 🏎️ and on larger datasets, still with a few lines of code 🖥️.
👉github: https://github.com/clementchadebec/benchmark_VAE
👉pypi: https://pypi.org/project/pythae/
https://preview.redd.it/jk4ukkgarpga1.png?width=1335&format=png&auto=webp&s=07c1ab2eaad104879637ad04472935d87baa31e9
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Hey guys, I’m the co-founder of a tech startup focused on providing free AI services. We’re one of the first mobile multipurpose AI apps.
We’ve developed a pretty cool app that offers AI services like image generation, code generation, image captioning, and more for free. We’re sort of like a Swiss Army knife of generative and analytical AI.
We’ve released a new feature called AAIA (Ask AI Anything), which is capable of answering all types of questions, even requests to generate literature, story-lines, jokes, general information, etc.
We’d love to have some people try it out, give us feedback, and keep in touch with us.
https://apps.apple.com/us/app/bright-eye/id1593932475
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https://medium.com/seeds-for-the-future/the-next-step-for-generative-ai-830112890d04?sk=1d6b4c96cc6cb0a4690bcf9df0d12bcc
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This post is co-written with Stephen Aylward, Matt McCormick, Brianna Major from Kitware and Justin Kirby from the Frederick National Laboratory for Cancer Research (FNLCR). Amazon SageMaker Studio Lab provides no-cost access to a machine learning (ML) development environment to everyone with an email address. Like the fully featured Amazon SageMaker Studio, Studio Lab allows […]
( 8
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Amazon SageMaker has announced the support of three new completion criteria for Amazon SageMaker automatic model tuning, providing you with an additional set of levers to control the stopping criteria of the tuning job when finding the best hyperparameter configuration for your model. In this post, we discuss these new completion criteria, when to use them, and […]
( 8
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AI Weirdness: the strange side of machine learning
( 2
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Announcements Machine Learning Controversy: From No-Code to No-Math One controversial topic in machine learning circles is code versus no-code. Can you be a real data scientist if you don’t code? Of course you can: You may be leveraging platforms and the code is one or two layers below the responsibilities of your job. Maybe you… Read More »DSC Weekly 7 February 2023 – Machine Learning Controversy: From No-Code to No-Math
The post DSC Weekly 7 February 2023 – Machine Learning Controversy: From No-Code to No-Math appeared first on Data Science Central.
( 21
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Data labeling and/or data annotation has long been a critical component of many machine learning and AI initiatives. In recent years, the demand for accurate and reliable data labeling has risen dramatically as the process becomes increasingly vital to the success of numerous projects. But what is data labeling exactly? Data Labeling 2023 – how… Read More »The Impact of Data Labeling 2023: Current Trends & Future Demands
The post The Impact of Data Labeling 2023: Current Trends & Future Demands appeared first on Data Science Central.
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Mobile Apps to Develop Your Data Science Skills -Mobile phones are the most preferred medium of accomplishing minute-to-minutest tasks on a daily basis. We don’t need to visit any particular restaurant to take away the food, we can do this by just sitting on our favorite couch at home, thanks to food ordering apps. Not… Read More »Best 9 Mobile Apps to Develop Your Data Science Skills in 2023
The post Best 9 Mobile Apps to Develop Your Data Science Skills in 2023 appeared first on Data Science Central.
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Doctors rarely make diagnoses based on a single factor — they look at a mix of data types, such as a patient’s symptoms, laboratory and radiology reports, and medical history. VinBrain, a Vietnam-based health-tech startup, is ensuring that AI diagnostics can take a similarly holistic view across vital signs, blood tests, medical images and more. Read article >
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AI Seinfeld Transphobic rant - YouTube
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I have made a Stack Overflow post here. I will highly appreciate all your help on this. Thank you!
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It took me about 46 hours to run this on my 3080 at home. The original files was from the Blu-ray release that was unfortunately pretty poorly done in my opinion. This version really gives it new life I think.
Here's a link to the video result to see for yourself:
https://vimeo.com/796411232
And a link to the model I used!
https://github.com/TencentARC/AnimeSR
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https://blog.google/technology/ai/bard-google-ai-search-updates/
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From the article:
Getty Images has filed a lawsuit in the US against Stability AI, creators of open-source AI art generator Stable Diffusion, escalating its legal battle against the firm.
The stock photography company is accusing Stability AI of “brazen infringement of Getty Images’ intellectual property on a staggering scale.” It claims that Stability AI copied more than 12 million images from its database “without permission ... or compensation ... as part of its efforts to build a competing business,” and that the startup has infringed on both the company’s copyright and trademark protections.
This is different from the UK-based news from weeks ago.
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I made an image captioning and clustering tools for computer vision and diffusion projects.
You can run almost everything automatically and with a simple CLI command. All contributions are welcome.
https://github.com/cobanov/image-clustering
https://github.com/cobanov/image-captioning
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A new tool brings the benefits of AI programming to a much broader class of problems.
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This blog post is co-written with Bruno Mateus, Jonathan Diedrich and Crispim Tribuna at Talkdesk. Contact centers are using artificial intelligence (AI) and natural language processing (NLP) technologies to build a personalized customer experience and deliver effective self-service support through conversational bots. This is the first of a two-part series dedicated to the integration of […]
( 8
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Researchers continue to develop new model architectures for common machine learning (ML) tasks. One such task is image classification, where images are accepted as input and the model attempts to classify the image as a whole with object label outputs. With many models available today that perform this image classification task, an ML practitioner may […]
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“I’ll tell you the problem with the scientific power that you’re using here: it didn’t require any discipline to attain it. You read what others had done and you took the next step. You didn’t earn the knowledge for yourselves, so you don’t take any responsibility for it. You stood on the shoulders of geniuses… Read More »It’s No Big Deal, but ChatGPT Changes Everything – Part III
The post It’s No Big Deal, but ChatGPT Changes Everything – Part III appeared first on Data Science Central.
( 24
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Just a few days ago, January 28, we celebrated Data Protection Day, an international event aimed at promoting data privacy and security. In line with the goal of raising awareness about data protection, it would be a good time to discuss data security with Realtime Operating System. This unconventional operating system is widely used, so… Read More »Ensuring Data Security in Realtime Operating System (RTOS) Devices
The post Ensuring Data Security in Realtime Operating System (RTOS) Devices appeared first on Data Science Central.
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A University of Toronto undergrad among an international team of researchers unleashing deep learning in the search for extraterrestrial civilizations.
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Tweet thread: https://twitter.com/WholeMarsBlog/status/1622139178439036928
First impressions: this sucks ass I can only ask about dogs and a few different types of prompts
Does anyone else have experiences to share with this nerfed LaMDA beta google released?
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https://youtu.be/ktdUeqzzhiA what text to speech does he use? he's been popping up on my yt feed lately and i can see he has different voices in his videos and most of them sound robotic, what do you think it's being used here?
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How can we move from an idea to production in AI?
Does the technology readiness levels (TRL) help?
If you want to get some answers please read this article in medium:
https://medium.com/towards-artificial-intelligence/technology-readiness-levels-trl-in-ai-development-c6ed1190fbd6
All the ideas are more than welcome!
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Hi all,
For my weekend project I figured I would build an AI driven spiritual successor to Mystery Science Theater 3000... Stop on by and watch the AI characters watch movies and make comments!
Today they are watching "The House on Haunted Hill" and "Plan 9 From Outer Space."
There's still a lot to do but I'm excited to play around with this more and see how it plays out and would love some feedback!
https://twitch.tv/MysteryAItheater
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https://www.udemy.com/course/chatgpt-bot/?couponCode=5-DAYS-FREE
Hey everyone, I recently made a course about ChatGPT as a fun passion project. This is for anyone who wants to learn how to create automated workflows (using Chrome extensions) with ChatGPT. Specifically, you will create a ChatGPT bot that automatically answers your emails. It is beginner friendly and includes getting some good practice with JavaScript. I hope you enjoy it and I'm looking forward to your feedback/questions :)
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https://www.youtube.com/watch?v=8TOgN-U0ask&t=1s
After the Lensa AI controversy led many people to question whether AI really is creative or is it just "remixing" other artists' copyrighted work used with permission, it has led many to wonder whether AI trained on copyrighted images should be illegal. This talk makes some interesting comparisons which might just mean the answer is no.
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From the Financial Times: https://www.ft.com/content/583ead66-467c-4bd5-84d0-ed5df7b5bf9c
Unpaywalled: https://archive.is/ciZPV
I guess I'm a little surprised, this feels like Google backing a competitor to 1) their own Google Brain teams, and 2) Deepmind. The cynical take might be that they're trying to lock in Anthropic; the same way Microsoft locked in OpenAI.
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Github: https://github.com/google/vizier
Google AI Blog: https://ai.googleblog.com/2023/02/open-source-vizier-towards-reliable-and.html
Tweet from Zoubin Ghahramani: https://twitter.com/ZoubinGhahrama1/status/1621321675936768000?s=20&t=ZEuz9oSc_GWYxixtXDskqA
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In this article (https://dallasinnovates.com/exclusive-qa-john-carmacks-different-path-to-artificial-general-intelligence/) there is a quote from John Carmack that read: "I asked Ilya Sutskever, OpenAI’s chief scientist, for a reading list. He gave me a list of like 40 research papers and said, ‘If you really learn all of these, you’ll know 90% of what matters today. "
My question is, what are these 40 papers?
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Can someone please help with this question - https://ai.stackexchange.com/questions/39029/why-does-advantage-learning-help-function-approximators
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This effort is focused on examining the behavior of reinforcement learning
systems in personalization environments and detailing the differences in policy
entropy associated with the type of learning algorithm utilized. We demonstrate
that Policy Optimization agents often possess low-entropy policies during
training, which in practice results in agents prioritizing certain actions and
avoiding others. Conversely, we also show that Q-Learning agents are far less
susceptible to such behavior and generally maintain high-entropy policies
throughout training, which is often preferable in real-world applications. We
provide a wide range of numerical experiments as well as theoretical
justification to show that these differences in entropy are due to the type of
learning being employed.
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Learning-based behavior prediction methods are increasingly being deployed in
real-world autonomous systems, e.g., in fleets of self-driving vehicles, which
are beginning to commercially operate in major cities across the world. Despite
their advancements, however, the vast majority of prediction systems are
specialized to a set of well-explored geographic regions or operational design
domains, complicating deployment to additional cities, countries, or
continents. Towards this end, we present a novel method for efficiently
adapting behavior prediction models to new environments. Our approach leverages
recent advances in meta-learning, specifically Bayesian regression, to augment
existing behavior prediction models with an adaptive layer that enables
efficient domain transfer via offline fine-tuning, online adaptation, or both.
Experiments across multiple real-world datasets demonstrate that our method can
efficiently adapt to a variety of unseen environments.
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The higher speed, scalability and parallelism offered by ReRAM crossbar
arrays foster development of ReRAM-based next generation AI accelerators. At
the same time, sensitivity of ReRAM to temperature variations decreases
R_on/Roff ratio and negatively affects the achieved accuracy and reliability of
the hardware. Various works on temperature-aware optimization and remapping in
ReRAM crossbar arrays reported up to 58\% improvement in accuracy and
2.39$\times$ ReRAM lifetime enhancement. This paper classifies the challenges
caused by thermal heat, starting from constraints in ReRAM cells' dimensions
and characteristics to their placement in the architecture. In addition, it
reviews available solutions designed to mitigate the impact of these
challenges, including emerging temperature-resilient DNN training methods. Our
work also provides a summary of the techniques and their advantages and
limitations.
( 2
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Hierarchical Clustering is a popular unsupervised machine learning method
with decades of history and numerous applications. We initiate the study of
differentially private approximation algorithms for hierarchical clustering
under the rigorous framework introduced by (Dasgupta, 2016). We show strong
lower bounds for the problem: that any $\epsilon$-DP algorithm must exhibit
$O(|V|^2/ \epsilon)$-additive error for an input dataset $V$. Then, we exhibit
a polynomial-time approximation algorithm with $O(|V|^{2.5}/
\epsilon)$-additive error, and an exponential-time algorithm that meets the
lower bound. To overcome the lower bound, we focus on the stochastic block
model, a popular model of graphs, and, with a separation assumption on the
blocks, propose a private $1+o(1)$ approximation algorithm which also recovers
the blocks exactly. Finally, we perform an empirical study of our algorithms
and validate their performance.
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Generative adversarial networks (GANs) have many application areas including
image editing, domain translation, missing data imputation, and support for
creative work. However, GANs are considered 'black boxes'. Specifically, the
end-users have little control over how to improve editing directions through
disentanglement. Prior work focused on new GAN architectures to disentangle
editing directions. Alternatively, we propose GANravel a user-driven direction
disentanglement tool that complements the existing GAN architectures and allows
users to improve editing directions iteratively. In two user studies with 16
participants each, GANravel users were able to disentangle directions and
outperformed the state-of-the-art direction discovery baselines in
disentanglement performance. In the second user study, GANravel was used in a
creative task of creating dog memes and was able to create high-quality edited
images and GIFs.
( 2
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Sparseness and robustness are two important properties for many machine
learning scenarios. In the present study, regarding the maximum correntropy
criterion (MCC) based robust regression algorithm, we investigate to integrate
the MCC method with the automatic relevance determination (ARD) technique in a
Bayesian framework, so that MCC-based robust regression could be implemented
with adaptive sparseness. To be specific, we use an inherent noise assumption
from the MCC to derive an explicit likelihood function, and realize the maximum
a posteriori (MAP) estimation with the ARD prior by variational Bayesian
inference. Compared to the existing robust and sparse L1-regularized MCC
regression, the proposed MCC-ARD regression can eradicate the troublesome
tuning for the regularization hyper-parameter which controls the regularization
strength. Further, MCC-ARD achieves superior prediction performance and feature
selection capability than L1-regularized MCC, as demonstrated by a noisy and
high-dimensional simulation study.
( 2
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We quantify the parameter stability of a spherical Gaussian Mixture Model
(sGMM) under small perturbations in distribution space. Namely, we derive the
first explicit bound to show that for a mixture of spherical Gaussian $P$
(sGMM) in a pre-defined model class, all other sGMM close to $P$ in this model
class in total variation distance has a small parameter distance to $P$.
Further, this upper bound only depends on $P$. The motivation for this work
lies in providing guarantees for fitting Gaussian mixtures; with this aim in
mind, all the constants involved are well defined and distribution free
conditions for fitting mixtures of spherical Gaussians. Our results tighten
considerably the existing computable bounds, and asymptotically match the known
sharp thresholds for this problem.
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Today, the NFL is continuing their journey to increase the number of statistics provided by the Next Gen Stats Platform to all 32 teams and fans alike. With advanced analytics derived from machine learning (ML), the NFL is creating new ways to quantify football, and to provide fans with the tools needed to increase their […]
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The National Football League (NFL) is one of the most popular sports leagues in the United States and is the most valuable sports league in the world. The NFL, BioCore, and AWS are committed to advancing human understanding around the diagnosis, prevention, and treatment of sports-related injuries to make the game of football safer. More […]
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I wanted to use the Learnable Trainangulation model in a commercial project. The source code itself is under MIT licensing. However, the dataset they have used is Human3.6M, which states that the license is "FREE OF CHARGE FOR ACADEMIC USE ONLY".
Yet, recent court rulings (in the US) state that models can use copyrighted data during training, and the results are no longer bound by that copyright (e.g. Google Books). Does the same apply here?
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Cheers to another year of cloud gaming! GeForce NOW celebrates its third anniversary with a look at how far cloud gaming has come, a community celebration and 25 new games supported in February. Members can celebrate all month long, starting with a sweet Dying Light 2 reward and support for nine more games this week, Read article >
( 7
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NVIDIA A100 Tensor Core GPUs running on Supermicro servers have captured leading results for inference in the latest STAC-ML Markets benchmark, a key technology performance gauge for the financial services industry. The results show NVIDIA demonstrating unrivaled throughput — serving up thousands of inferences per second on the most demanding models — and top latency Read article >
( 6
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For several years, NVIDIA has been working with some of the world’s leading financial institutions to develop and execute a wide range of rapidly evolving AI strategies. For the past three years, we’ve asked them to tell us collectively what’s on the top of their minds. Sometimes the results are just what we thought they’d Read article >
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https://www.axios.com/2023/02/01/chatgpt-subscriptions-chatbot-openai
Not fully paywalled, but there's a tiering system.
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GitHub (sadly without weights). https://github.com/PetchMa/ML_GBT_SETI
News.
https://www-scinexx-de.translate.goog/news/kosmos/seti-findet-acht-potenzielle-alien-signale/?_x_tr_sl=de&_x_tr_tl=en&_x_tr_hl=de&_x_tr_pto=wapp
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This is laughable.They were sitting on all of the technology.And now they scramble to do something better than 10 links.I for myself will be disappointed with anything less than movie Her.
It's a high bar.May be.I would not expect personality.May be some rudementary memory.But the ability to perform almost any digital task must be there.It can be built in a garage using open source projects.COME ON.Some good programmers and hackathon.Yes I am waiting for stability ai model.Or may be gpt 3 API can be used.But
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In this blog post, we will take a closer look at the implications of ChatGPT’s authorship, the role of AI in scientific literature, and…
Continue reading on Becoming Human: Artificial Intelligence Magazine »
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Linear & Logistic: The Relationship Between Regression Models
Continue reading on Becoming Human: Artificial Intelligence Magazine »
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Hello and welcome to the blog! My name is ChatGPT, and I am a large language model trained by OpenAI.
P.S. This article includes a use…
( 9
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More than $1 million in funding available to selected Solver teams and fellows.
( 7
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Almost 80% of today’s web content is user-generated, creating a deluge of content that organizations struggle to analyze with human-only processes. The availability of consumer information helps them make decisions, from buying a new pair of jeans to securing home loans. In a recent survey, 79% of consumers stated they rely on user videos, comments, […]
( 10
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Recent developments in deep learning have led to increasingly large models such as GPT-3, BLOOM, and OPT, some of which are already in excess of 100 billion parameters. Although larger models tend to be more powerful, training such models requires significant computational resources. Even with the use of advanced distributed training libraries like FSDP and […]
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Hi guys,
I have made a video on YouTube here where I explain how deltas and delta-deltas features are computed. These are used quite a lot in speech recognition systems.
I hope it may be of use to some of you out there. As always, feedback is more than welcomed! :)
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Things are a lot sunnier these days for designers looking to visualize their projects in NVIDIA Omniverse, a platform for creating and operating metaverse applications.
( 6
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Artificial intelligence is the new electricity. The fifth industrial revolution. And companies that go all-in on AI are reaping the rewards. So how do you make that happen? That big question — how? — is explored by Nitin Mittal, principal at Deloitte, one of the world’s largest professional services organizations, and co-author Thomas Davenport in Read article >
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Openai is developing a new tool to help distinguish between AI-written and human-written text. Here is an unofficial python wrapper of openai model to detect if a text is written by #chatgpt , #gpt3 , #gpt etc
Github: https://github.com/promptslab/openai-detector
https://preview.redd.it/f45ggu45tgfa1.png?width=1122&format=png&auto=webp&s=4cb5ae70d7194cc3c070f3ad2dcbc968a804d4a3
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I would like to invite interested people to collaborate on this hobby project of mine.
This is still in an early-stage, and I believe it can be significantly improved together.
The GitHub repository link is here: https://github.com/kayuksel/multi-rl-crowd-sim
Note: The difference from StarCraft is that Dragons can hide behind each other.
They also reduce their strength of hitting, propotional to decrease of their health.
https://preview.redd.it/wrpcaz782dfa1.png?width=640&format=png&auto=webp&s=1dede69acb78e874a80bd532af85b269c7117f9f
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Analyst reports. Academic papers. Ph.D. programs. There are a lot of places you can go to get a glimpse of the future. But the best place might just be El Coyote Cojo, a whiskey-soaked dive bar that doesn’t exist in real life. Fire up Cyberpunk 2077 and you’ll see much more than the watering hole’s Read article >
( 6
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Broadcasters have an arsenal of new features and technologies at their disposal; the eighth-generation NVIDIA video encoder on RTX 40 Series GPUs with support for the open AV1 video-coding format; new NVIDIA Broadcast app effects like Eye Contact and Vignette; and support for AV1 streaming in Discord.
( 7
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We’re launching a classifier trained to distinguish between AI-written and human-written text.
We’ve trained a classifier to distinguish between text written by a human and text written by AIs from a variety of providers. While it is impossible to reliably detect all AI-written text, we believe
( 3
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Announcements Data Models for the Weather With January coming to an end, we here in the Northeast let out a collective sigh of relief as the month ends without any major snowstorms that tend to happen in the first month of the year. Weather forecasting is a centuries-old practice that has its roots in divination… Read More »DSC Weekly 31 January 2023 – Data Models for the Weather
The post DSC Weekly 31 January 2023 – Data Models for the Weather appeared first on Data Science Central.
( 19
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In the previous article, we looked at two Ever-Successful NFL teams, the Kansas City Chiefs and the San Francisco 49ers, who seem to be able to win consistently even while things change around them and players and coaches come and go. Then, we looked at two Never-Successful teams, the Arizona Cardinals and the Cleveland Browns,… Read More »Exploding vs. Imploding: What the NFL Has to Teach Us About Managing Agile Enterprises, Part II
The post Exploding vs. Imploding: What the NFL Has to Teach Us About Managing Agile Enterprises, Part II appeared first on Data Science Central.
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I'v been thinking a lot about Marshall McLuhan and his 4 laws of media. Specifically, the one that states that all new forms of media cause something to be retrieved from the past. What will ChatGPT and AI revive and retrieve? I put some more thoughts in my blog. Would love to hear your thoughts on it.
https://bobhutchins.substack.com/p/what-media-format-will-chatgpt-and
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1D: MusicLM, VALL-E
2D: Stable Diffusion, DALL-E, MidJourney
3D (or 2+1D): Imagen-video, Phenaki
3D: Magic3D, DreamFusion, Point-E
4D (or 3+1D): Make-A-Video-3D
[Searchcolab] What’s next? 🤔
https://reddit.com/link/10p6vw9/video/gqbnrsaxh7fa1/player
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https://ainewsbase.com/google-musiclm-copyright-issues-not-releasing/
The samples they do show might just sound weird because of the stored file or whatever but the sound definitely sounds kinda weird.
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https://peltarion.com/blog/data-science/towards-a-token-free-future-in-nlp
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I’m an ML Engineer at Hive AI and I’ve been working on a ChatGPT Detector.
Here is a free demo we have up: https://hivemoderation.com/ai-generated-content-detection
From our benchmarks it’s significantly better than similar solutions like GPTZero and OpenAI’s GPT2 Output Detector. On our internal datasets, we’re seeing balanced accuracies of >99% for our own model compared to around 60% for GPTZero and 84% for OpenAI’s GPT2 Detector.
Feel free to try it out and let us know if you have any feedback!
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In order to improve my talking skills, I am doing a little series on how to setup Stable Diffusion on Paperspace, and I am astounded how much time it takes to do the audio editing. Well, part of the reason is that I've only been doing this for 3 days and my process is very inefficient, but it feels that in the current time, neural nets should be able to do things like remove uhms, lip smacking and breath intakes.
I've looked around, and this post from 9 years ago says the only choice is to edit it by hand. Is that still true?
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From the given link!, I gather that it is a large-scale Transformer trained to use digital tools like a web browser. Right now, it’s hooked up to a Chrome extension which allows it to observe what’s happening in the browser and take certain actions, like clicking, typing, and scrolling, etc.
I am interested in knowing the broad steps involved in building something like this.
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https://github.com/tysam-code/hlb-CIFAR10
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During the 1970s, Ethernet pioneer and 3Com Internet equipment company founder Bob Metcalfe was working on something called the “Data Reconfiguration Service” for the early Internet. “It was an effort to write a special purpose programming language to convert data formats, Metcalfe said during a 2021 OriginTrail.io panel session. “And the goal was so that… Read More »Enabling contextual computing in today’s enterprise information fabrics
The post Enabling contextual computing in today’s enterprise information fabrics appeared first on Data Science Central.
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Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning (ML) practitioners get started on training and deploying ML models quickly. You can use these algorithms and models for both supervised and unsupervised learning. They can process various types of input data, including tabular, […]
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Amazon Forecast is a fully managed service that uses machine learning (ML) to generate highly accurate forecasts, without requiring any prior ML experience. Forecast is applicable in a wide variety of use cases, including estimating supply and demand for inventory management, travel demand forecasting, workforce planning, and computing cloud infrastructure usage. You can use Forecast […]
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Things the video covers:
What is intelligence? What is A.I.? What is the best currently available and what are the benefits? How does it work? What are the downsides? The increasing speed of human technological advancement Why A.I. actually terrifies me! (Some scenarios)
I hope you enjoy it!
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https://youtu.be/Y6gXZ61NnOE
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I recently created a website called https://cashwithai.com that is dedicated to helping people learn how to make money using AI like ChatGPT. The website offers a variety of resources, including a QuickStart guide, case studies, and tips and tricks for monetizing AI-generated content.
Additionally, I'm offering free 1-on-1 consultations to anyone who is looking for personalized advice and guidance on how to make money with AI. I'm not running ads or charging; I run purely off donations.
Let me know if you have any questions!
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Hi everyone, I made a JupyterLab extension to use OpenAI’s GPT models for code and text completion on your notebook cells.
This extension passes your current notebook cell to the GPT API and completes your code/text for you. You can customize the GPT parameters in the Advanced Settings menu.
I made this extension when I couldn't find any Copilot/Codex extensions for JupyterLab. It doesn't make sense that ML folks don't have an easy way to use AI generated code in their own tools. VS Code does allow you use Copilot, but I've gotten used to Jupyter and a lot of ML/DS folks I know still prefer using Jupyter over VS code.
Installation
pip install gpt_jupyterlab
GitHub Repo: https://github.com/henshinger/gpt-jupyterlab/
Demo
GPT JupyterLab Demo
Note: You will need your own OpenAI API Key to use this extension.
Would love to get your feedback!
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I am building an open-source ML observability and refinement toolkit which recently got investment from YCombinator.
The tool helps ML practitioners to: 1. Understand how their models are performing in production 2. Catch edge-cases and outliers to help them refine their models 3. Allow them to customise the tool according to their needs (hence, open-source) 4. Bring data-security at the forefront (hence, self hosted)
You can check out the project https://github.com/uptrain-ai/uptrain and would love to hear feedback from the community
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https://pypi.org/project/rwkvstic/
Currently supports tensorflow, pytorch, jax
Also has support for tensor streaming, 8bit jit-quant and multi-gpu.
Run RWKV 7B on 8GB of vram or 14B on 16GB of vram.
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Bright Eye: mobile AI app that generates art, code, poems, essays, short stories, answers questions, and more!
Hey guys, I’m the cofounder of a tech startup focused on providing free AI services. We’re one of the first mobile multipurpose AI apps.
We’ve developed a pretty cool app that offers AI services like image generation, code generation, image captioning, and more for free. We’re sort of like a Swiss Army knife of generative and analytical AI.
We’ve released a new feature called AAIA(Ask AI Anything), which is capable of answering all types of questions, even requests to generate literature, storylines, answer questions and more, (think of chatgpt).
We’d love to have some people try it out, give us feedback, and keep in touch with us.
https://apps.apple.com/us/app/bright-eye/id1593932475
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https://www.youtube.com/watch?v=Vw-t826JcDQ
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Specifically this one:
https://www.youtube.com/watch?v=MFv7apjatwM&ab_channel=Lux-Topic
If there is no current AI that is able to listen to a song and write down the lyrics accurately, then I provide this idea freely.
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University of Florida - Warrington College of Business's Mo Wang offers advice for the future of work.
Full Story: https://explore.research.ufl.edu/the-future-of-work.html#ai-hiring
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I'm looking into projects which augment the RLHF training approach of chatGPT with explicit rules, such as in https://paperswithcode.com/paper/constitutional-ai-harmlessness-from-ai.
Ideally there would be both rules and priority levels between the rules, similarly to the Asimov laws of robotics.
The Open-Assistant project (https://github.com/LAION-AI/Open-Assistant) captures the spirit, but it is looking to replicate chatGPT at the moment.
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Find the release notes here:
https://github.com/nnaisense/evotorch/releases/tag/v0.4.0
A big highlight is how fast these implementations are! I genuinely believe GPU-acceleration is the future of Evolutionary algorithms, and EvoTorch and its integration into the PyTorch ecosystem is a fantastic enabler for this.
To demonstrate the raw speed provided by the new release, I compared EvoTorch's CMA-ES implementation to that provided by the popular pycma package on the 80-dimensional Rastrigin problem and tracked the run-time:
Performance was measured over 50 runs on the 80-dimensional Rastrigin problem
The crazy thing to note is that when we switch to GPU (Tesla V100), we can efficiently run CMA-ES with population sizes going into 100k+!
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Could someone please help with this - https://ai.stackexchange.com/questions/38894/are-there-papers-that-do-an-empirical-investigation-on-drl-hyperparameters
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This post is co-authored by Tristan Miller from Best Egg. Best Egg is a leading financial confidence platform that provides lending products and resources focused on helping people feel more confident as they manage their everyday finances. Since March 2014, Best Egg has delivered $22 billion in consumer personal loans with strong credit performance, welcomed […]
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GeForce NOW RTX 4080 SuperPODs are rolling out now, bringing RTX 4080-class performance and features to Ultimate members — including support for NVIDIA Ada Lovelace GPU architecture technologies like NVIDIA DLSS 3. This GFN Thursday brings updates to some of GeForce NOW’s hottest games that take advantage of these amazing technologies, all from the cloud. Read article >
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Hello everyone,
I am a student in AP Research. For my project, I am conducting a survey to analyze the connection between science fiction and technology (specifically Artificial Intelligence). This survey (linked) asks a few questions about your knowledge of Sci-fi, Artificial Intelligence, and the connection between the two. It should not take more than 10 minutes of your time.
If you are interested, the link to the form is below:
https://docs.google.com/forms/d/e/1FAIpQLScY_VaNI-CEtTiJiLHgYCCguEZ7m9DUdQoxvFTjXFFLOGu2KA/viewform
If you have additional questions, my email is in the linked google form. Thank you for your participation, it is deeply appreciated!
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In this video I explain about INSTRUCTOR, an instruction-finetuned text embedding model that can generate text embeddings tailored to any task (e.g., classification, retrieval, clustering, text evaluation, etc.) and domains (e.g., science, finance, etc.) by simply providing the task instruction, without any finetuning. Instructor achieves sota on 70 diverse embedding tasks! I also show a google collab demo of instructor
https://youtu.be/vg38cq3KJ6M
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In the context of digital transformation and innovation, there is no lack of “hot topics” to discuss. Emerging technologies are truly emerging everywhere. What is most exciting – and what demonstrates their greatest promise – is that these new technologies are converging to produce innovative new businesses, products, and services. Over the past decade, we… Read More »Innovation at the Convergence of Emerging Technologies: Business at the Edge
The post Innovation at the Convergence of Emerging Technologies: Business at the Edge appeared first on Data Science Central.
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In a recent article on Autonomous Intelligent Systems (AIS) [1], Ajit Joakar described various features and characteristics of such systems, including associated technologies and research areas, building blocks and core elements, critical factors for success, and cross-cutting enablers. He introduces AIS as an “emerging interdisciplinary field that deals with situations where humans interact with AI systems… Read More »Five Principles of Safe Driving in AIS (Autonomous Intelligent Systems)
The post Five Principles of Safe Driving in AIS (Autonomous Intelligent Systems) appeared first on Data Science Central.
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We stand at the threshold of a new era of precision medicine, where health and life sciences data hold the potential to dramatically propel and expand our understanding and treatment of human disease. One of the tools that we believe will help to enable precision medicine is Terra, the secure biomedical research platform co-developed by […]
The post Biomedical Research Platform Terra Now Available on Microsoft Azure appeared first on Microsoft Research.
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Today, gaining customer loyalty cannot be a one-off thing. A brand needs a focused and integrated plan to retain its best customers—put simply, it needs a customer loyalty program. Earn and burn programs are one of the main paradigms. A typical earn and burn program rewards customers after a certain number of visits or spend. […]
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Model explainability refers to the process of relating the prediction of a machine learning (ML) model to the input feature values of an instance in humanly understandable terms. This field is often referred to as explainable artificial intelligence (XAI). Amazon SageMaker Clarify is a feature of Amazon SageMaker that enables data scientists and ML engineers […]
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In November 2022, we announced that AWS customers can generate images from text with Stable Diffusion models in Amazon SageMaker JumpStart. Today, we announce a new feature that lets you upscale images (resize images without losing quality) with Stable Diffusion models in JumpStart. An image that is low resolution, blurry, and pixelated can be converted […]
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As its name suggests, Orbital Sidekick is creating technology that acts as a buddy in outer space, keeping an eye on the globe using satellites to help keep it safe and sustainable. The San Francisco-based startup, a member of the NVIDIA Inception program, enables commercial and government users to optimize sustainable operations and security with Read article >
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Jensen Huang headlines Stockholm AI confab, Berzelius supercomputer upgraded to 94 NVIDIA DGX A100 systems.
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Hey, everyone!
I'd like to show you an experiment that we did with ChatGPT - we generated about 1000 resumes of famous people. Each resume is being generated from a single ChatGPT prompt - no human input was done to the resumes other than the prompt and it's the same prompt for every resume - the only difference is the name of the person.
Here's a preview: https://thisresumedoesnotexist.com/
I'd like to hear your thoughts as it's in a very early stage and there's a lot of work to be done.
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Announcements When AI Gets Going, the Going Gets Weird Last week, Microsoft announced its third investment in OpenAI. This time it’s a multi-billion dollar deal, with plans to harness OpenAI’s ChatGPT in Microsoft’s product lines, including Bing. I’m smiling as I’m typing because I’m still thinking about Bill Schmarzo’s lead in Part 1 of 2… Read More »DSC Weekly 24 January 2023 – When AI Gets Going, the Going Gets Weird
The post DSC Weekly 24 January 2023 – When AI Gets Going, the Going Gets Weird appeared first on Data Science Central.
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Warehouse robotics is witnessing steady growth, driven by the increasing adoption of automated solutions in storage for food and beverages, consumer goods, retail, and third-party logistics. The collaboration between the e-commerce sector and warehouse robotics is also a major driver of this market, as it allows for developing increasingly sophisticated warehouse automation systems. Additionally, the… Read More »Revolutionizing the Supply Chain: Developments in the Warehouse Robotics Industry
The post Revolutionizing the Supply Chain: Developments in the Warehouse Robotics Industry appeared first on Data Science Central.
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In Part I of the blog series “It’s No Big Deal, but ChatGPT Changes Everything”, we were introduced into the world of ChatGPT, chatbots, and generative Artificial Intelligence (AI). We ended Part I by giving ChatGPT a test run, by asking it “What would be a great vacation place for my family?” that gives us… Read More »It’s No Big Deal, but ChatGPT Changes Everything – Part II
The post It’s No Big Deal, but ChatGPT Changes Everything – Part II appeared first on Data Science Central.
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Sweden is outfitting its AI supercomputer for a journey to the cutting edge of machine learning, robotics and healthcare. It couldn’t ask for a better guide than Anders Ynnerman (above). His signature blue suit, black spectacles and gentle voice act as calm camouflage for a pioneering spirit. Early on, he showed a deep interest in Read article >
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Artist Ducky 3D creates immersive experiences through vibrant visuals and beautiful 3D environments in the alien-inspired animation Stylized Alien Landscape — this week In the NVIDIA Studio.
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Now I wrote that the bot would use the Markov Decision Process. Would that be correct? And if not why?
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Hi, I have created this tutorial on how to train yolov8 object detection model on a custom dataset. Please have a look at: https://youtu.be/ZzC3SJJifMg
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This blog contains type of joins like Inner join, Left join, Right join , Full join, Self join and Cross join.
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This blog contains Window function in SQL like (Rank, Dense_Rank, Row_Number , Lead, Lag) .
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ChatGPT, a language model developed by OpenAI, has the potential to revolutionize a wide range of industries and change the way we…
Continue reading on Becoming Human: Artificial Intelligence Magazine »
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Human resource management (HRM) is a critical aspect of any organization as it involves managing the workforce and ensuring that their…
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Who are we?
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Data Science
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Deodel is a Python implementation of a classifier with native support for mixed attributes data. It features good accuracy, especially with heterogenous attributes. It even supports mixing of continuous and nominal values in the same attribute column.
https://github.com/c4pub/deodel
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Computer scientists want to know the exact limits in our ability to clean up, and reconstruct, partly blurred images.
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Add AI to the list of defenses against identity attacks, one of the most common and hardest breach to prevent. More than 40% of all data compromises involved stolen credentials, according to the 2022 Verizon Data Breach Investigations Report. And a whopping 80% of all web application breaches involved credential abuse. “Credentials are the favorite Read article >
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For the past 500 years, the National Library of Sweden has collected virtually every word published in Swedish, from priceless medieval manuscripts to present-day pizza menus. Thanks to a centuries-old law that requires a copy of everything published in Swedish to be submitted to the library — also known as Kungliga biblioteket, or KB — Read article >
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We're happy to announce that OpenAI and Microsoft are extending our partnership.
This multi-year, multi-billion dollar investment from Microsoft follows their previous investments in 2019 and 2021, and will allow us to continue our independent research and develop AI that is increasingly safe, useful, and powerful.
In pursuit
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It’s an early version and I’m trying to get some feedback on how I can improve this and do it the “right way”.
Source Code and Results: https://github.com/prabhuomkar/bitbeast/tree/master/ptibench
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For example see,
https://gymnasium.farama.org/tutorials/training_agents/reinforce_invpend_gym_v26/
The REINFORCE algorithm takes the state to produce the mean and sd of a normal distribution from which the action is sampled.
state = torch.tensor(np.array([state])) action_means, action_stddevs = self.net(state) # create a normal distribution from the predicted # mean and standard deviation and sample an action distrib = Normal(action_means[0] + self.eps, action_stddevs[0] + self.eps) action = distrib.sample()
In deployment however, wouldn't it make sense to just use action_means directly? I can see reasons to use random sampling in certain environments where a non-deterministic strategy is optimal (like rock-paper-scissors). But generally speaking is taking the action_means directly in deployment a thing?
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Hi guys,
I have made a video on YouTube here where I discuss about why deep neural networks fail to beat tree-based models on tabular datasets.
I hope it may be of use to some of you out there. As always, feedback is more than welcomed! :)
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https://madgenius.co
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Hi all, i saw an ad for an AI service that takes my picture and change it with AI to add bokeh and change background, to make it look profesional-quality for a dating profile.
However i believe it was charging 19$ and i'm sure it could be found for free (An article mentions "BeFake", but it's only for Apple devices.?
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I'm trying to find an AI to remove watermarks from imagens like the ones below:
https://i.imgur.com/JlyfJXs.png
https://i.imgur.com/YKU3Qku.png
I already tried almost all online services, and a couple of softwares that must be installed.
The results were all terrible =[
Any suggestons?
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Hi guys,
I have made a video on YouTube here where I discuss about why deep neural networks fail to beat tree-based models on tabular datasets.
I hope it may be of use to some of you out there. As always, feedback is more than welcomed! :)
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https://github.com/google-research/tuning_playbook - Google has released a playbook (solely) about how to tune hyper-parameters of neural networks.
Disclaimer: I am unrelated to this repository, just came across it and thought it is suitable for this subreddit. I have searched through and found no posts, thus I post it to hear some comments/insights from you ;)
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https://time.com/6247678/openai-chatgpt-kenya-workers/
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We covered this in our newsletter today. Here it is verbatim-- if you find it useful, hit the link and sub: https://smokingrobot.beehiiv.com/p/ai-wars
Microsoft has dominated BIG TECH headlines over the last few months, thanks largely to a drumbeat of headlines involving their partner OpenAI and its world-shaping ChatGPT.
So awe-striking is Microsoft's hand right now, it has made rival companies' advancements, like Apple's recently announced and insanely powerful M2 MacBook Pros, look pedestrian in comparison.
But now Google has entered the chat.
And by "entered the chat", we mean that CEO Sundar Pichai - Pich-AI? - released a distressingly long 15,000-word(!) treatise on its own endeavors in AI, signaling a counter attack... maybe... at some point in the future... when and if it…
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A Good Road Map To Machine Learning enginner
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Time series forecasting is the process of using a model to predict future values of a time series based on its past values.
Continue reading on Becoming Human: Artificial Intelligence Magazine »
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This post is co-written by Christopher Diaz, Sam Kinard, Jaime Hidalgo and Daniel Suarez from CCC Intelligent Solutions. In this post, we discuss how CCC Intelligent Solutions (CCC) combined Amazon SageMaker with other AWS services to create a custom solution capable of hosting the types of complex artificial intelligence (AI) models envisioned. CCC is a […]
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Deep-learning model takes a personalized approach to assessing each patient’s risk of lung cancer based on CT scans.
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Reinforcement learning provides a conceptual framework for autonomous agents to learn from experience, analogously to how one might train a pet with treats. But practical applications of reinforcement learning are often far from natural: instead of using RL to learn through trial and error by actually attempting the desired task, typical RL applications use a separate (usually simulated) training phase. For example, AlphaGo did not learn to play Go by competing against thousands of humans, but rather by playing against itself in simulation. While this kind of simulated training is appealing for games where the rules are perfectly known, applying this to real world domains such as robotics can require a range of complex approaches, such as the use of simulated data, or instrumenting real-wo…
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Hi all!
I'd like to share an open source project that I am currently working on together with a few colleagues: DocArray!
If you've ever trained models that deal with different data types (images, text, video, audio, ...) then you know how much of a hassle it can be to keep track of all of your tensors, what shapes they have, and what data they are meant to represent.
That's what we're trying to change with DocArray, a Python library for representing, sending, and storing multi-modal data!
The core idea of DocArray is that you define Documents that represent your data. For example, one Document could hold the file path to an image, its image tensor, and and image embedding that your model creates. A different Document could do the same thing for some Text, and a third Document might co…
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🌟 Synthcity isa library for generating and benchmarking synthetic tabular data. https://github.com/vanderschaarlab/synthcity
🚀 Synthcity includes a wide range of algorithms for various use cases, such as:
- tabular data(CTGAN, TVAE, Bayesian Networks etc)
- survival analysis(SurvivalGAN etc).
- time series(Fourier Flows, TimeGAN, etc.).
- privacy-focused(DP-GAN, PATEGAN, AdsGAN, DECAF).
- domain adaptation(RadialGAN).
🔍 Synthcity supports benchmarking multiple algorithms, testing data quality, downstream performance, statistical fidelity, and privacy metrics.
🌀 Give it a try:
- Library: https://github.com/vanderschaarlab/synthcity
- Tutorial: https://colab.research.google.com/drive/1Vr2PJswgfFYBkJCm3hhVkuH-9dXnHeYV?usp=sharing
- Docs: https://synthcity.readthedocs.io/
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I am currently using the Google Cloud Model Registry and I want to learn what you use for archiving your machine learning models. What are the other options for developers who have to store hundreds of models?
https://cloud.google.com/blog/products/ai-machine-learning/vertex-ai-model-registry
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Robots are finally getting a grip. Developers have been striving to close the gap on robotic gripping for the past several years, pursuing applications for multibillion-dollar industries. Securely gripping and transferring fast-moving items on conveyor belts holds vast promise for businesses. Soft Robotics, a Bedford, Mass., startup, is harnessing NVIDIA Isaac Sim to help close Read article >
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The Ultimate upgrade begins today: GeForce NOW RTX 4080 SuperPODs are now rolling out, bringing a new level of high-performance gaming to the cloud. Ultimate members will start to see RTX 4080 performance in their region soon, and experience titles like Warhammer 40,000: Darktide, Cyberpunk 2077, The Witcher 3: Wild Hunt and more at ultimate Read article >
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https://preview.redd.it/k3ims1d6zuca1.png?width=2324&format=png&auto=webp&v=enabled&s=4f4bbe410508bdd4c45f45e55dd5c1ea0fcb5fcc
You must have heard about ChatGPT. Maybe you heard that it was trained with RLHF and PPO. Perhaps you do not really understand how that process works. Then check my Gist on Reinforcement Learning from Human Feedback (RLHF): https://gist.github.com/JoaoLages/c6f2dfd13d2484aa8bb0b2d567fbf093
No hard maths, straight to the point and simplified. Hope that it helps!
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A new experiential learning opportunity challenges undergraduates across the Greater Boston area to apply their AI skills to a range of industry projects.
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Businesses are moving towards AI Software Products. In fact, a recent study proves this claim by saying that nine out of ten companies…
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I am working with 64x64x64 voxel arrays and am running into significant problems with GPU memory management. I am using TensorFlow and have an NVIDIA GeForce RTX 4080 MSI Ventus edition with 16GB of memory (purchased using research grant funding... it's sitting in a hacked together eGPU setup lol). It performs beautifully on 32x32x32 data but I can't even get started with the larger data format. I have tried limiting GPU data utilization per process, as per this post and limiting memory growth, as per this post (Ctrl+F "second option"). I have 64GB of RAM so I can fit the data into memory (even though I know that's not efficient) and was trying to put that data in a TensorFlow Dataset object, in which, according to the docs, "iteration happens in a streaming fashion, so the full dataset do…
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A few days ago, I responded to a post on LinkedIn about how Google seems to always find a way to keep ahead of the pack, even when someone of importance leaves the company. It occurred to me that NFL teams have to adapt and remake themselves from season to season, as players and coaches… Read More »Ever-Successful vs. Never-Successful: What the NFL Has to Teach Us About Managing Agile Enterprises, Part I
The post Ever-Successful vs. Never-Successful: What the NFL Has to Teach Us About Managing Agile Enterprises, Part I appeared first on Data Science Central.
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For insights into the future of generative AI, check out the latest episode of the NVIDIA AI Podcast. Host Noah Kravitz is joined by Pat Grady and Sonya Huang, partners at Sequoia Capital, to discuss their recent essay, “Generative AI: A Creative New World.” The authors delve into the potential of generative AI to enable Read article >
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As any new mom or dad can tell you, parenting can be a challenge — packed with big worries and small hassles. But it may be about to get a little bit easier thanks to Glüxkind Technologies and their smart stroller, Ella. The company has just been named a CES 2023 Innovation Awards Honoree for Read article >
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To celebrate the upcoming Lunar New Year holiday, NVIDIA artist Zhelong Xu, aka Uncle Light, brought Chinese zodiac signs to life this week In the NVIDIA Studio — modernizing the ancient mythology in his signature style.
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Are you sure your Conversational AI solution is on the right path?
Our chatbot evaluation metrics pinpoint if your solution leveraging the best of the industry’s leading practices, meeting user expectations, and fully taking advantage of the available technology to ensure frictionless and efficient experiences.
https://masterofcode.com/chatbot-analysis-framework
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From the article:
Getty Images is suing Stability AI, creators of popular AI art tool Stable Diffusion, over alleged copyright violation.
In a press statement shared with The Verge, the stock photo company said it believes that Stability AI “unlawfully copied and processed millions of images protected by copyright” to train its software and that Getty Images has “commenced legal proceedings in the High Court of Justice in London” against the firm.
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The faster the video - the better Yolo is!
https://www.linkedin.com/posts/maltsevanton_basically-any-yolon1-vs-yolon-comparison-activity-7021021466506768384-y7Se?utm_source=share&utm_medium=member_desktop
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I made this little python package a while ago but realized I never shared it here. Maybe it's useful to you: https://github.com/bloomen/featureimpact
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Here is a podcast episode with Sugandha Sharma from MIT where we discuss how memories can be implemented, control theory, and much more!
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What to make of Deepmind’s Sparrow: Is it a sparrow or a hawk? ie a chatGPT killer Recently, Demis Hassabis from DeepMind has been urging caution (DeepMind’s CEO Helped Take AI Mainstream. Now He’s Urging Caution Time magazine/Davos) DeepMind also announced a new chat engine called Sparrow – supposedly a chatGPT killer Sparrow is not… Read More »What to make of Deepmind’s Sparrow: Is it a sparrow or a hawk?
The post What to make of Deepmind’s Sparrow: Is it a sparrow or a hawk? appeared first on Data Science Central.
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The hotel industry is competitive, and it is solely dependent on customer satisfaction. Customers are key. The hotel industry knows this and the importance of the NPS score for customer satisfaction. A better NPS score means satisfied/loyal customers. What hotels have in their control is the website user interface, menu, and providing a seamless customer… Read More »What is a Good Net Promoter Score for the Hotel/Resort Industry?
The post What is a Good Net Promoter Score for the Hotel/Resort Industry? appeared first on Data Science Central.
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By 2025, more than 80% of enterprises will shift from traditional data centers to the cloud or third-party colocation data centers. For most businesses, data is an irreplaceable asset and a key investment area for future growth. Virtual colocation is becoming the talk of how data centers are shifting to adapt to growing business environments.… Read More »7 Reasons Why Fast-Growing Businesses Are Turning to Virtual Colocation in 2023
The post 7 Reasons Why Fast-Growing Businesses Are Turning to Virtual Colocation in 2023 appeared first on Data Science Central.
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Amazon SageMaker Studio is a fully integrated development environment (IDE) for machine learning (ML) partly based on JupyterLab 3. Studio provides a web-based interface to interactively perform ML development tasks required to prepare data and build, train, and deploy ML models. In Studio, you can load data, adjust ML models, move in between steps to adjust experiments, […]
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Amazon SageMaker JumpStart is the Machine Learning (ML) hub of SageMaker providing pre-trained, publicly available models for a wide range of problem types to help you get started with machine learning. Understanding customer behavior is top of mind for every business today. Gaining insights into why and how customers buy can help grow revenue. Customer churn is […]
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In their largest-ever joint AI initiative, NVIDIA and Dell Technologies today launched a wave of Dell PowerEdge systems available with NVIDIA acceleration, enabling enterprises to efficiently transform their businesses with AI. A total of 15 next-generation Dell PowerEdge systems can draw from NVIDIA’s full AI stack — including GPUs, DPUs and the NVIDIA AI Enterprise Read article >
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in this newsletter like halfway down
how long before this just turns into the Dark Knight surveillance scenario?
apparently you can use wifi antennas to track people
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Hi there! Making my first contribution to this subreddit and sharing something you probably haven't heard of (I might be wrong though :)
So, this is about AI host for Live quizzes. The AI generated avatar can run a live quiz and doesn't need any pre-made scripts or your help. Works only on Facebook Live, Youtube and Twitch.
What do you think?
https://preview.redd.it/94gb6wmc6fca1.png?width=1080&format=png&auto=webp&s=a14bfd0b1140e887d1b7c615fd0ef00e88373ce6
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Hey,
Over the past couple months, I've been collecting AI tools & generators and decided to put them into a website. The result is SaaS AI Tools, a growing collection of 400+ generative AI tools to help supercharge your creativity and take your business to the next level.
Also, to differentiate a bit - I've added another section that involves a feed of daily AI articles, so you can keep up-to-date on the top AI headlines. This is how I'm personally keeping up with all the AI stuff today. I'll be adding more tools and news sources soon.
I've launched the website on Product Hunt and would appreciate any of your support 🙏
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The legendary Tim Dettmers has updated his blog on which GPU to purchase for Deep learning to include advice for the latest GPU series:
https://timdettmers.com/2023/01/16/which-gpu-for-deep-learning/
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The Lunar Lander environment uses the box2d engine to simulate physics. I was wondering if there is code somewhere which explicitly models the environment as as state-space model?
LunarLander code: https://github.com/openai/gym/blob/master/gym/envs/box2d/lunar_lander.py
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I'm trying to solve pick&place (and possibly also the other tasks in this repository) with Franka Emika Panda manipulator simulated in Mujoco. I've tried for long with stable_baseline3 but without any results, someone told me to try with RLLib because has better implementation (?), but still I can't find any solution...
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In this blog, I have explained how you can use my library modoptima to optimize the YOLO v7 model improving the inference speed by 3-10 times on good processors.
https://medium.com/@vikasojha894/pruning-and-quantizing-yolo-v7-with-modoptima-19c61aff7301
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Hello Reddit community,
I am faced with some Finance coursework and I would appreciate any help or guidance from experienced practitioners in the ML/Finance industry.
I have a panel dataset (Data File) - that includes information such as date, stock name, market-cap, sector, and a column for the target variable which is the monthly absolute return of the stock in percentage terms (i.e one month from the date column).
I am tasked with building a predictive model to forecast the target variable.
I would like to know information on which ML model you would recommend, and why?
Thank you in advance for any help or guidance provided.
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I needed a C++ wrapper library around libsvm and liblinear using Eigen so I made one. Maybe it's useful for you as well: https://github.com/bloomen/svmegn
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Check out this medium article about how to create such an app: https://medium.com/towards-artificial-intelligence/how-to-create-an-end-2-end-text-paraphrase-app-db83a4e05918
Or check this repository about Quotera a paraphrase app to be deployed via Streamlit or FastAPI and Docker in Python: https://github.com/stavrostheocharis/quotera
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https://link.medium.com/HnMPSMA4zwb
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Hey guys, I’m the cofounder of a tech startup focused on providing free AI services.
We’ve developed a pretty cool app that offers AI services like image generation, code generation, image captioning, and more for free.
We’re sort of like a Swiss Army knife of generative and analytical AI.
We’ve released a new feature called AAIA(Ask AI Anything), which is capable of answering all types of questions, even requests to generate literature (fantasy, folklore, drama, fiction, fable, etc). It’s sort of like chat-gpt.
We’d appreciate it if you could try it out and let us know your thoughts: https://apps.apple.com/us/app/bright-eye/id1593932475
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There are incredible applications built using AI.
It is definitely a trend that the world should not ignore.
We started to maintain a collection of cool ai projects in Github: https://github.com/ai-collection/ai-collection
Our mission is to increase reach and visibility for these awesome projects!
It is updated daily and we hope that with the help of the community, it will be a great source for discovering AI applications.
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AI Weirdness: the strange side of machine learning
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This post is co-written with Jennifer Bergstrom, Sr. Technical Director, ParsonsX. Parsons Corporation (NYSE:PSN) is a leading disruptive technology company in critical infrastructure, national defense, space, intelligence, and security markets providing solutions across the globe to help make the world safer, healthier, and more connected. Parsons provides services and capabilities across cybersecurity, missile defense, space ground […]
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This post is co-written by Ramdev Wudali and Kiran Mantripragada from Thomson Reuters. In 1992, Thomson Reuters (TR) released its first AI legal research service, WIN (Westlaw Is Natural), an innovation at the time, as most search engines only supported Boolean terms and connectors. Since then, TR has achieved many more milestones as its AI […]
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This blog post is co-written with Chaoyang He and Salman Avestimehr from FedML. Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at a single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a […]
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This blog post is co-written with Chaoyang He and Salman Avestimehr from FedML. Analyzing real-world healthcare and life sciences (HCLS) data poses several practical challenges, such as distributed data silos, lack of sufficient data at any single site for rare events, regulatory guidelines that prohibit data sharing, infrastructure requirement, and cost incurred in creating a […]
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These skills might not be visiable but they are important for ML and DL
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One of the biggest AI discoveries over the past year has been the importance of human feedback for building next-gen LLMs — but I still see a lot of confusion around how RLHF works at a fundamental level.
I wrote a blog to get into the details here: https://www.surgehq.ai/blog/introduction-to-reinforcement-learning-with-human-feedback-rlhf-series-part-1
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The Open Deep Learning Toolkit for Robotics version 2.0 was just released! This new version of the toolkit includes several improvements, such as new tools for object detection, efficient continual inference, tracking, emotion estimation and high-resolution pose estimation. Furthermore, this version includes a refined ROS interface, along with support for ROS2.
You can download it here: https://github.com/opendr-eu/opendr
We look forward to receiving your feedback, bug reports, and suggestions for improvements!
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Have a look at this medium article:
https://pub.towardsai.net/how-to-create-a-python-package-for-fetching-weather-data-b17614627f30
And the corresponding repository:
https://github.com/stavrostheocharis/weather_data_retriever
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New fellows are working on health records, robot control, pandemic preparedness, brain injuries, and more.
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This post was co-authored with Mark Lott, Distinguished Technical Architect, Salesforce, Inc. Enterprises that operate globally are experiencing challenges sourcing customer support professionals with multi-lingual experience. This process can be cost-prohibitive and difficult to scale, leading many enterprises to only support English for chats. Using human interpreters for translation support is expensive, and infeasible since […]
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This is guest post by Andy Whittle, Principal Platform Engineer – Application & Reliability Frameworks at The Very Group. At The Very Group, which operates digital retailer Very, security is a top priority in handling data for millions of customers. Part of how The Very Group secures and tracks business operations is through activity logging […]
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Using a pretrained AI model from NVIDIA, startup Evozyne created two proteins with significant potential in healthcare and clean energy. A joint paper released today describes the process and the biological building blocks it produced. One aims to cure a congenital disease, another is designed to consume carbon dioxide to reduce global warming. Initial results Read article >
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GFN Thursday kicks each weekend off with new games and updates straight from the cloud. This week adds more games from publisher THQ Nordic to the GeForce NOW library, as part seven total additions. Members can gear up to play these new titles the ultimate way with the upcoming release of the new Ultimate membership, Read article >
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The global retail industry has a $100 billion problem. “Shrinkage” — the loss of goods due to theft, damage and misplacement — significantly crimps retailers’ profits. An estimated 65% of shrinkage is due to theft, according to the National Retail Federation’s 2022 Retail Security Survey, conducted in partnership with the Loss Prevention Research Council. And Read article >
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Hey guys, I’m the cofounder of a tech startup focused on providing free AI services.
We’ve developed a pretty cool app that offers AI services like image generation, code generation, image captioning, and more for free. We’re sort of like a Swiss Army knife of generative and analytical AI.
In light of the chatgpt bug going on rn, check us out and stay in touch with us:
https://apps.apple.com/us/app/bright-eye/id1593932475
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In the nearest future, tens of thousands of self-driving cars may be on the roads. Big companies like BMW and Tesla continue to invest.
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Advancement in AI has shaken multiple grounds at the same time. What does it mean for designers?
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This post is co-authored by Marios Skevofylakas, Jason Ramchandani and Haykaz Aramyan from Refinitiv, An LSEG Business. Financial service providers often need to identify relevant news, analyze it, extract insights, and take actions in real time, like trading specific instruments (such as commodities, shares, funds) based on additional information or context of the news item. […]
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I am trying to implement a custom PettingZoo environment, and a shared policy with Stable Baselines 3. I am running into trouble with the action spaces not being compatible, since PettingZoo has started using gymnasium instead of gym. Does anyone know if these libraries no longer work together, and perhaps if there is a work-around?
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3D and animation extraordinaire CG Geek completed an ambitious design challenge this week In the NVIDIA Studio — building a massive, sci-fi-inspired 3D world in only three days
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OpenAI researchers collaborated with Georgetown University’s Center for Security and Emerging Technology and the Stanford Internet Observatory to investigate how large language models might be misused for disinformation purposes. The collaboration included an October 2021 workshop bringing together 30 disinformation researchers, machine learning experts, and policy analysts, and
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MIT researchers developed and studied a customized AI training program for users with varied backgrounds, which could be delivered across large organizations.
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Story here: https://www.bloomberg.com/news/articles/2023-01-10/microsoft-weighs-10-billion-chatgpt-investment-semafor-says?srnd=premium
Unpaywalled: https://archive.ph/XOOlg
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I have a set of ~100 topic categories, and I want to determine which are semantically close to a text input.
I've found several implementations, but I know some (LDA) are already obsolete. OpenAI's text-embedding-ada-002 model just came out so I'm wondering if that's the best option now.
Other topic modeling implementations:
Multi-Class Text Classification with Doc2Vec & Logistic Regression
Build taxonomy-based contextual targeting using AWS Media Intelligence and Hugging Face BERT
Topic Modeling with BERTopic
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The demand for web and app development is rising as there are significant improvements in the field of technology every year. More and more people are establishing careers in the field of development as a result of advancements in technologies and frameworks. As a result, the data from Statista says that by 2024, there will… Read More »Flexible Engagement Model to Hire Full-Stack Developers: A 2023 Guide
The post Flexible Engagement Model to Hire Full-Stack Developers: A 2023 Guide appeared first on Data Science Central.
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AI is at the heart of humanity’s most transformative innovations — from developing COVID vaccines at unprecedented speeds and diagnosing cancer to powering autonomous vehicles and understanding climate change. Virtually every industry will benefit from adopting AI, but the technology has become more resource intensive as neural networks have increased in complexity. To avoid placing Read article >
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Amazon SageMaker is a fully managed machine learning (ML) service. With SageMaker, data scientists and developers can quickly and easily build and train ML models, and then directly deploy them into a production-ready hosted environment. It provides an integrated Jupyter authoring notebook instance for easy access to your data sources for exploration and analysis, so […]
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If you’ve had the opportunity to build a search application for unstructured data (i.e., wiki, informational web sites, self-service help pages, internal documentation, etc.) using open source or commercial-off-the-shelf search engines, then you’re probably familiar with the inherent accuracy challenges involved in getting relevant search results. The intended meaning of both query and document can […]
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Improve Tortoise TTS by 30% inference speed, and packaged it up as a hosted API that charges per-second. All code is open-sourced: https://github.com/metavoicexyz/tortoise-tts-modal-api, https://github.com/metavoicexyz/tortoise-tts
It can be used via a UI on: https://tts.themetavoice.xyz
There are more details here: https://twitter.com/vatsal\_aggarwal/status/1612536547248836608?s=20
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This is the first episode of a new podcast on machine learning featuring Maarten Grootendorst.
Maarten Grootendorst: BERTopic, Data Science, Psychology | Learning from Machine Learning #1
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Introduction:
Continue reading on Becoming Human: Artificial Intelligence Magazine »
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This is part 3 of a three-part series on the Economics of Ethics. In Part I of the Economics of Ethic series, we talked about economics as a framework for the creation and distribution of society value. In Part II, we discussed the difference between financial and economic measures, the role of laws and regulations… Read More »Economics of Ethics: Is Ethics Ultimately an Economics Conversation? Part III
The post Economics of Ethics: Is Ethics Ultimately an Economics Conversation? Part III appeared first on Data Science Central.
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Back before he retired, Naval War College professor and contributor to The Atlantic Tom Nichols published a 2017 book called The Death of Expertise. Those who claim their own facts or knowledge without supporting evidence, he noted, have become more and more prominent in online conversation we’ve been having. And the noisiest and most prone… Read More »Data science and the death of (all but narrow) AI expertise in 2023
The post Data science and the death of (all but narrow) AI expertise in 2023 appeared first on Data Science Central.
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Machine learning (ML) applications are complex to deploy and often require the ability to hyper-scale, and have ultra-low latency requirements and stringent cost budgets. Use cases such as fraud detection, product recommendations, and traffic prediction are examples where milliseconds matter and are critical for business success. Strict service level agreements (SLAs) need to be met, […]
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Sponsored Post Attend the Data Science Symposium 2022 on November 8 The Center for Business Analytics at the University of Cincinnati will present its annual Data Science Symposium 2022 on November 8. This all day in-person event will have three featured speakers and two tech talk tracks with four concurrent presentations in each track. The […]
The post Attend the Data Science Symposium 2022, November 8 in Cincinnati appeared first on Machine Learning Mastery.
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